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Deep generative models provide a powerful solution for the de novo design of molecules. However, the majority of existing methods only generate molecules for a single target. Generating molecules with biological activities against multiple specific targets and desired properties remains an extremely difficult challenge. In this study, we propose a novel 3D molecule generation framework based on reinforcement

Recent progress in computational biology has driven the development of machine learning models for predicting protein post-translational modification sites. However, challenges such as data imbalance and limited sequence-context representation continue to hinder prediction accuracy, particularly for less frequent modifications like succinylation. In this study, we propose RLSuccSite, a reinforcement

Persistent air quality pollution poses a serious threat to human health, and is one of the action points that policy makers should monitor according to the Directive 2008/50/EC. While deploying a massive network of hyperlocal sensors could provide extensive monitoring, this approach cannot generate geospatial continuous data and present several challenges in terms of logistics. Thus, developing accurate

We introduce PoLiGenX, a novel generative model for de novo ligand design that employs latent-conditioned, target-aware equivariant diffusion. Our approach leverages the conditioning of the ligand generation process on reference molecules located within a specific protein pocket. By doing so, PoLiGenX generates shape-similar ligands that are adapted to the target pocket, enabling effective applications

While chemoinformatics is a well-established scientific field, its integration into university curricula is rarely discussed. In this work, we share our experience in developing a chemoinformatics curriculum at the University of Strasbourg and highlight the main challenges in higher education for this discipline.

Protein fitness prediction plays a crucial role in the advancement of protein engineering endeavours. However, the combinatorial complexity of the protein sequence space and the limited availability of assay-labelled data hinder the efficient optimization of protein properties. Data-driven strategies utilizing machine learning methods have emerged as a promising solution, yet their dependence on labelled

Atom mapping involves identifying the correspondence between individual atoms in reactant molecules and their counterparts in product molecules. This process is crucial for gaining deeper insight into reaction mechanisms, such as defining reaction templates and determining which chemical bonds are formed or broken during a reaction. However, reliable atom mapping data are often limited or incomplete

The many-body expansion (MBE) of the lattice energy enables an ab initio description of molecular solids using correlated wave function approximations. However, the practical application of MBE requires computing the large number of n-body contributions efficiently. To this end, we employ a multi-level coupled-cluster approach which adapts the approximation level based on interaction type and intermolecular

Channelrhodopsins (ChRs) are photoreceptors that function as light-gated ion channels. Over the last two decades, they have become essential tools in optogenetics, enabling precise manipulation of neurons, neural circuits, and animal behavior through light. Although structural studies have provided important mechanistic insights into channelrhodopsins, a detailed understanding of their ion conduction

Self-assembled nanotubes (SCPNs) formed by alternating chirality α-Cyclic Peptides (d,l-α-CPs) have presented interesting biological applications, such as antimicrobial activity or ion transmembrane transport. Due to difficulties to follow these processes with experimental techniques, Molecular Dynamics (MD) simulations have been commonly used to understand the mechanism that led to their biological

Inspired by the linearized adiabatic connection (AC0) theory, an approximation to second-order N-electron valence state perturbation theory (NEVPT2) has been developed, termed NEVPT within singles (NEVPTS). This approach utilizes amplitudes derived from approximate single-excitation wave functions, requiring only 3rd-order reduced density matrices (RDMs). Consequently, it avoids the computational bottleneck

We present a hybrid classical-quantum computational pipeline for the determination of adsorption energies of a benzotriazole molecule on an aluminum alloy surface relevant for the transport industry, in particular to address the corrosion problem. The molecular adsorbate and substrate alloy were selected by interrogating molecular and materials databases, in search for desired criteria. The protocol

Exploring the interaction configurations between substrates and atomic or molecular systems is crucial for various scientific and technological applications, such as characterizing catalytic reactions, solvation structures, and molecular interactions. Traditional approaches for generating substrate-reactant configurations often rely on chemical intuition, symmetry operations, or random initial states

We introduce OxPot, a comprehensive open-access data set comprising over 15 thousand chemically diverse organic molecules. Leveraging the precision of DFT-derived highest occupied molecular orbital energies (EHOMO), OxPot serves as a robust platform for accelerating the prediction of oxidation potential (Eox). Using the PBE0 hybrid functional and cc-pVDZ basis set, we establish a strong near-linear

The performance and reliability of machine learning (ML)-quantitative structure-property relationship (QSPR) models depend on the quality, size, and diversity of the data set used for model training. In this study, we manually curated a large-scale data set containing 3120 donor-acceptor (D-A) conjugated polymers (CPs) by selecting the most utilized 60 donors and 52 acceptors. This data set serves

The coaxial spool structure of DNA has been regarded as an equilibrium conformation inside of a viral capsid. It has also been accepted that the DNA conformation inside the viral capsid should correlate strongly with the ejection of DNA out of the viral capsid. However, how the coaxial spool structure of DNA would affect the ejection kinetics remains elusive at the molecular level. In this study, we

Glasses are widely used for their various applications, which arise from their inherent lack of long-range ordering. This characteristic makes it challenging to describe their atomic properties. To facilitate and accelerate glass research, computational simulations, such as molecular dynamics or Monte Carlo simulations, are commonly employed to model the structure of these amorphous materials. However

The global potential energy surface (PES) search of large molecular systems remains a significant challenge in chemistry due to "the curse of dimensionality". To address this, here we develop a rigid-body chain method in the framework of a stochastic surface walking (SSW) global optimization method, termed rigid-body chain SSW (RC-SSW). Based on the angle-axis representation for a single rigid body

The study of intrinsically disordered proteins (IDPs) and their role in biomolecular condensate formation has become a critical area of research, offering insights into fundamental biological processes and therapeutic development. Here, we present IPAMD (Intrinsically disordered Protein Aggregation Molecular Dynamics), a plugin-based software designed to simulate the formation dynamics of biomolecular

Deep learning models have demonstrated their potential in learning effective molecular representations critical for drug property prediction and drug discovery. Despite significant advancements in leveraging multimodal drug molecule semantics, existing approaches often struggle with challenges such as low-quality data and structural complexity. Large language models (LLMs) excel in generating high-quality

Effective drug safety assessment, guided by the 3R principle (Replacement, Reduction, Refinement) to minimize animal testing, is critical in early drug development. Drug-induced liver injury (DILI), particularly drug-induced cholestasis (DIC), remains a major challenge. This study introduces a computational method for predicting DIC by integrating PubChem substructure fingerprints with biological data

Electronic Lab Notebooks (ELNs) have become indispensable tools for modern research laboratories, facilitating data management, collaboration, and documentation of scientific experiments. However, the proliferation of diverse ELN platforms poses challenges for researchers who need to seamlessly exchange data between different systems. In this paper, we present ELNdataBridge, a novel server-based solution

We present Moldrug, a computational tool for accelerating the hit-to-lead phase in structure-based drug design. Moldrug explores the chemical space using structural modifications suggested by the CReM library and by optimizing an adaptable fitness function with a genetic algorithm. Moldrug is complemented by Moldrug-Dashboard, a cross-platform and user-friendly graphical interface tailored for the

Predicting surfactant adsorption using the currently available isotherm model is limited to one or two independent variables: equilibrium concentration and temperature. This study aims to develop an adsorption model that includes molecular features, testing conditions, and solid properties in the model. A Physics-Informed Neural Network (PINN) was structured by integrating adsorption isotherm into

Similarity searching is a mainstay in cheminformatics that is generally used to identify compounds with desired properties. For small molecular fragments, similarity calculations based on standard descriptors often have limited utility for establishing meaningful similarity relationships due to feature sparseness. As an alternative, we have adapted the concept of context-depending word pair similarity

Untargeted metabolomics can comprehensively map the chemical space of a biome, but is limited by low annotation rates (< 10%). We used chemical characteristics vectors, consisting of molecular fingerprints or chemical compound classes, predicted from mass spectrometry data, to characterize compounds and samples. These chemical characteristics vectors (CCVs) estimate the fraction of compounds with specific

Efficient and reliable prediction of molecular properties, such as water solubility, hydration free energy, lipophilicity, and quantum mechanical properties, is essential for rational compound design in the chemical and pharmaceutical industries. While Graph Neural Networks (GNNs) have significantly advanced molecular property prediction tasks, their high memory footprint, computational demands, and

Molecular dynamics simulations are nowadays one of the key methods to investigate the (thermo)dynamics of protein-ligand binding at atomic resolution. The calculation of binding free energies of charged species is an encountered problem in molecular dynamic simulations. This is due to the approximation of the long-range electrostatic interaction. Here, we explore the discrepancies and biases of different

Nucleoside analogues (NAs) constitute a class of compounds that mirror the structure of natural nucleosides but undergo chemical modifications, rendering them an important compound resource for drug development as exogenous substitutes for natural nucleosides. Nevertheless, the design of novel NA-based drugs remains a great challenge due to the complexity of asymmetric synthesis and restrained chemical

The optimal enzyme pH is a critical factor that directly influences the catalytic efficiency of the enzymes. Accurate computational prediction of the optimal pH can greatly advance our understanding and design of enzymes for diverse scientific and industrial applications. However, current prediction tools often fall short in terms of accuracy and robustness. In this study, we propose OpHReda, a novel

Suppression of Tumorigenicity 2 (ST2) is a member of the IL-1 receptor family, which includes transmembrane (ST2L) and soluble (sST2) isoforms. sST2 functions as a decoy receptor for Interleukin-33 (IL-33), thereby blocking the activation of the IL-33/ST2L signaling axis, which is essential for tissue repair and immune regulation. Clinical evidence indicates that elevated sST2 levels are associated

To fulfill functions for differentially regulating the downstream signaling pathways, functional ligands (i.e., agonists or antagonists) targeting nuclear receptors (NRs) are designed to stabilize different conformations (active or inactive) of the proteins. However, in practical applications, it is usually difficult to determine the molecular category of an NR ligand because these molecules all bind

This work presents the extension of the semi‐empirical density functional tight binding method, DFTB3, to include zirconium for biomedical and material science applications. The parametrization of Zr has been carried out in consistency with already established 3OB parameters including the elements C, H, N, O, S, P, Mg, Zn, Na, K, Ca, F, Cl, Br, and I. Zirconium‐ligand association and reaction energies

We conducted a double‐hierarchical ab initio benchmark and DFT performance study of the organodichalcogenide bonding motif CH3Ch1Ch2(O)nCH3 with Ch1, Ch2 = S, Se and n = 0, 1, 2. The organodichalcogenide model systems were optimized at ZORA‐CCSD(T)/ma‐ZORA‐def2‐TZVPP. Our ab initio benchmark involved a hierarchical series of all‐electron relativistically contracted variants of the Karlsruhe basis

The field of molecular representation has witnessed a shift towards models trained on molecular structures represented by strings or graphs, with chemical information encoded in nodes and bonds. Graph-based representations offer a more realistic depiction and support 3D geometry and conformer-based augmentation. Graph Neural Networks (GNNs) and Graph-based Transformer models (GTs) represent two paradigms